An integrated system for managing logistics ports using AI
Abstract
The logistics ports sector is witnessing continuous development in light of the increasing challenges associated with managing operations and improving operational efficiency. This research aims to develop a smart system based on artificial intelligence (AI) and deep learning techniques to automatically detect truck plates entering and exiting the port, which enables the registration and tracking of vehicles with high accuracy and speed. The system relies on image processing and automatic number plate recognition (ANPR) techniques to capture and analyze incoming vehicle data, in addition to analyzing data and conducting monthly statistics on truck movement, waiting times, and logistics operations performance. The research presents a practical model of the proposed system, addressing the technical aspects of integrating artificial intelligence with port management systems. The effectiveness of the system is also tested on a sample of real data and the results are analyzed to measure recognition accuracy and processing speed. This study contributes to improving the operational efficiency of ports by reducing human intervention, enhancing safety, and achieving smarter and more sustainable logistics management.
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URN: https://sloi.org/urn:sl:tjoee101351
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